The segmenting of objects in images by automatic image analysis is normally highly complex, insoluble without the assistance of a human operator. The reason for this is that there is no universal criterion at image level for characterizing the object and separating it from its environment, since a semantic object is liable to be made up of a number of regions of widely differing colours and textures.
The segmenting of semantic objects finds various applications in widely varying fields and, in particular:                in film post-production, for touching up colours limited to objects, or for isolating an object in a sequence in order to embed it in another sequence,        in video coding, to enhance the compression ratio by coding the object in a single frame then transmitting only its changes of position,        in video indexing, in order to extract semantically relevant information regarding the content of the images.        
Methods of segmenting semantic objects based on the active contours (“snakes”) formalism are known, consisting in having an initial approximation of the contour of the object evolve by latching onto the faces of the image, while satisfying the regularity constraints of the contour curve.
Also known are methods of segmenting objects based on the colour, classifying the pixels of the image or a region of the image in pixels of the object and pixels of the background based on their location in a colour space.